{"doi":"10.1101/2023.12.02.569717","title":"Benchmarking computational methods to identify spatially variable genes and peaks","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>Spatially resolved transcriptomics offers unprecedented insight by enabling the profiling of gene expression within the intact spatial context of cells, effectively adding a new and essential dimension to data interpretation. To efficiently detect spatial structure of interest, an essential step in analyzing such data involves identifying spatially variable genes. Despite researchers having developed several computational methods to accomplish this task, the lack of a comprehensive benchmark evaluating their performance remains a considerable gap in the field. Here, we present a systematic evaluation of 14 methods using 60 simulated datasets generated by four different simulation strategies, 12 real-world transcriptomics, and three spatial ATAC-seq datasets. We find that spatialDE2 consistently outperforms the other benchmarked methods, and Moran’s I achieves competitive performance in different experimental settings. Moreover, our results reveal that more specialized algorithms are needed to identify spatially variable peaks.</jats:p>","journal":null,"year":null,"id":636389,"datarank":0.515098080672772,"base_score":3.4339872044851463,"endowment":3.4339872044851463,"self_citation_contribution":0.515098080672772,"citation_network_contribution":0.0,"self_endowment_contribution":0.515098080672772,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":30,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1651627,"name":"Zain M.Patel","orcid":null,"position":1,"is_corresponding":false},{"id":557612,"name":"Dongyuan Song","orcid":"0000-0003-1114-1215","position":2,"is_corresponding":false},{"id":987364,"name":"Guanao Yan","orcid":"0000-0002-6861-1882","position":3,"is_corresponding":false},{"id":86014,"name":"Jingyi Jessica Li","orcid":"0000-0002-9288-5648","position":4,"is_corresponding":false},{"id":2737,"name":"Luca Pinello","orcid":"0000-0003-1195-9607","position":5,"is_corresponding":false},{"id":1016891,"name":"Zhijian Li","orcid":"0000-0003-1979-2645","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Benchmarking computational methods to identify spatially variable genes and peaks","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>Spatially resolved transcriptomics offers unprecedented insight by enabling the profiling of gene expression within the intact spatial context of cells, effectively adding a new and essential dimension to data interpretation. To efficiently detect spatial structure of interest, an essential step in analyzing such data involves identifying spatially variable genes. Despite researchers having developed several computational methods to accomplish this task, the lack of a comprehensive benchmark evaluating their performance remains a considerable gap in the field. Here, we present a systematic evaluation of 14 methods using 60 simulated datasets generated by four different simulation strategies, 12 real-world transcriptomics, and three spatial ATAC-seq datasets. We find that spatialDE2 consistently outperforms the other benchmarked methods, and Moran’s I achieves competitive performance in different experimental settings. Moreover, our results reveal that more specialized algorithms are needed to identify spatially variable peaks.</jats:p>","is_dataset_classified":null,"base_score":3.4339872044851463,"endowment":3.4339872044851463,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"38076922","pmcid":null,"openalex_id":"https://openalex.org/W4389289559","authors":[],"funders":[{"funder_name":"National Institutes of Health","grant_id":"5R35HG010717-03","title":"Multiscale exploration of the functional non-coding genome"},{"funder_name":"NHGRI NIH HHS","grant_id":"R35 HG010717","title":null}],"total_grants":2,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[{"year":2024,"count":10},{"year":2025,"count":14},{"year":2026,"count":6}],"oa_status":"green","license":"cc-by-nc-nd","oa_locations":[{"url":"https://www.biorxiv.org/content/biorxiv/early/2023/12/03/2023.12.02.569717.full.pdf","host_type":"repository"},{"url":"https://www.biorxiv.org/content/biorxiv/early/2023/12/03/2023.12.02.569717.full.pdf","host_type":"repository"},{"url":"https://syndication.highwire.org/content/doi/10.1101/2023.12.02.569717","host_type":"publisher"},{"url":"https://doi.org/10.1101/2023.12.02.569717","host_type":"repository"},{"url":"https://pubmed.ncbi.nlm.nih.gov/38076922","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10705556","host_type":"repository"},{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10705556/pdf/nihpp-2023.12.02.569717v1.pdf","host_type":"repository"},{"url":"http://dx.doi.org/10.1101/2023.12.02.569717","host_type":""}],"fields_of_study":["Single-cell and spatial transcriptomics","Gene expression and cancer classification","Advanced Fluorescence Microscopy Techniques","0206 medical engineering","02 engineering and technology"],"mesh_terms":[],"keywords":["Benchmarking","Profiling (computer programming)","Benchmark (surveying)","Computer science","Variable (mathematics)","Data mining","Context (archaeology)","Field (mathematics)","Machine learning","Artificial intelligence","Biology","Mathematics","Geography","Cartography","Article"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-06T16:56:21.168918Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}